Application of electronic nose as a non-invasive technique for odor fingerprinting and detection of bacterial foodborne pathogens: a review

被引:72
作者
Bonah, Ernest [1 ,2 ]
Huang, Xingyi [1 ]
Aheto, Joshua Harrington [1 ]
Osae, Richard [1 ]
机构
[1] Jiangsu Univ, Sch Food & Biol Engn, Xuefu Rd 301, Zhenjiang 212013, Jiangsu, Peoples R China
[2] Food & Drugs Author, Lab Serv Dept, POB CT 2783, Cantonments Accra, Ghana
来源
JOURNAL OF FOOD SCIENCE AND TECHNOLOGY-MYSORE | 2020年 / 57卷 / 06期
基金
中国国家自然科学基金;
关键词
Sensors; Pattern recognition; Foodborne pathogens; Volatile organic compounds (VOCs); Electronic nose; SUPPORT VECTOR MACHINES; TOTAL VIABLE COUNTS; VOLATILE COMPOUNDS; CHEMICAL SENSORS; ALFALFA SPROUTS; GAS SENSOR; CLASSIFICATION; IDENTIFICATION; CONTAMINATION; QUALITY;
D O I
10.1007/s13197-019-04143-4
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
Food safety issues across the global food supply chain have become paramount in promoting public health safety and commercial success of global food industries. As food regulations and consumer expectations continue to advance around the world, notwithstanding the latest technology, detection tools, regulations and consumer education on food safety and quality, there is still an upsurge of foodborne disease outbreaks across the globe. The development of the Electronic nose as a noninvasive technique suitable for detecting volatile compounds have been applied for food safety and quality analysis. Application of E-nose for pathogen detection has been successful and superior to conventional methods. E-nose offers a method that is noninvasive, fast and requires little or no sample preparation, thus making it ideal for use as an online monitoring tool. This manuscript presents an in-depth review of the application of electronic nose (E-nose) for food safety, with emphasis on classification and detection of foodborne pathogens. We summarise recent data and publications on foodborne pathogen detection (2006-2018) and by E-nose together with their methodologies and pattern recognition tools employed. E-nose instrumentation, sensing technologies and pattern recognition models are also summarised and future trends and challenges, as well as research perspectives, are discussed.
引用
收藏
页码:1977 / 1990
页数:14
相关论文
共 94 条
  • [1] The detection of foodborne bacteria on beef: the application of the electronic nose
    Abdallah, Soad A.
    Al-Shatti, Laila A.
    Alhajraf, Ali F.
    Al-Hammad, Noura
    Al-Awadi, Bashayer
    [J]. SPRINGERPLUS, 2013, 2 : 1 - 9
  • [2] Probabilistic support vector machines for multi-class alcohol identification
    Acevedo, F. J.
    Maldonado, S.
    Dominguez, E.
    Narvaez, A.
    Lopez, F.
    [J]. SENSORS AND ACTUATORS B-CHEMICAL, 2007, 122 (01): : 227 - 235
  • [3] Determination of trimethylamine in milk using an MS based electronic Nose
    Ampuero, S
    Zesiger, T
    Gustafsson, V
    Lundén, A
    Bosset, JO
    [J]. EUROPEAN FOOD RESEARCH AND TECHNOLOGY, 2002, 214 (02) : 163 - 167
  • [4] Healthy scents: microbial volatiles as new frontier in antibiotic research?
    Avalos, Mariana
    van Wezell, Gilles P.
    Raaijmakers, Jos M.
    Garbeva, Paolina
    [J]. CURRENT OPINION IN MICROBIOLOGY, 2018, 45 : 84 - 91
  • [5] Independent component analysis-processed electronic nose data for predicting Salmonella typhimurium populations in contaminated beef
    Balasubramanian, S.
    Panigrahi, S.
    Logue, C. M.
    Doetkott, C.
    Marchello, M.
    Sherwood, J. S.
    [J]. FOOD CONTROL, 2008, 19 (03) : 236 - 246
  • [6] Balasubramanian S, 2016, ELECTRONIC NOSES AND TONGUES IN FOOD SCIENCE, P59, DOI 10.1016/B978-0-12-800243-8.00007-X
  • [7] Investigation of Different Gas Sensor-Based Artificial Olfactory Systems for Screening Salmonella typhimurium Contamination in Beef
    Balasubramanian, Sundar
    Amamcharla, Jayendrakumar
    Panigrahi, Suranjan
    Logue, Catherine M.
    Marchello, Martin
    Sherwood, Julie S.
    [J]. FOOD AND BIOPROCESS TECHNOLOGY, 2012, 5 (04) : 1206 - 1219
  • [8] Balbin JR, 2017, 2017 7TH IEEE INTERNATIONAL CONFERENCE ON CONTROL SYSTEM, COMPUTING AND ENGINEERING (ICCSCE), P247, DOI 10.1109/ICCSCE.2017.8284413
  • [9] Black tea classification employing feature fusion of E-Nose and E-Tongue responses
    Banerjee, Mahuya Bhattacharyya
    Roy, Runu Banerjee
    Tudu, Bipan
    Bandyopadhyay, Rajib
    Bhattacharyya, Nabarun
    [J]. JOURNAL OF FOOD ENGINEERING, 2019, 244 : 55 - 63
  • [10] Berna Z, 2013, ELECT NOSE FAST GC D, P1255, DOI [10.17660/ActaHortic.2013.1012.169, DOI 10.17660/ACTAH0RTIC.2013.1012.169]